pycmprsk
A Python port of the R package cmprsk. Estimation, testing and regression modeling of subdistribution functions in competing risks:
cuminc- non-parametric cumulative incidence functions, with Gray's k-sample test across groups and stratification.crr- Fine-Gray subdistribution-hazards regression, with time-fixed covariates (cov1), time-varying covariates (cov2 + tf), per-group censoring weights (cengroup), and the full Huber/White sandwich variance including theq(u)correction for the estimated censoring distribution.predict_crr,summary_crr,timepoints,plot_cuminc,plot_predict- the same downstream API as R.
This package's functionality is numerically validated against R's cmprsk. See
Parity testing below.
Install
pip install pycmprsk
The package is pure Python; the hot loops are JIT-compiled with numba.
Quick example
The arrays below were dumped from R using RNGversion("1.6.2"); set.seed(2)
and the same data setup as cmprsk/tests/test.R. The resulting plots
visually match R's plot(cuminc(...)) and plot(predict(crr(...))).
import matplotlib.pyplot as plt
import numpy as np
from pycmprsk import crr, cuminc, plot_cuminc, plot_predict, predict_crr
def tf_quad(uft):
"""Match R's ``function(uft) cbind(uft, uft^2)``."""
uft = np.asarray(uft, dtype=np.float64)
return np.column_stack([uft, uft**2])
Data arrays (Click to expand - Data sourced from R's test suite)
ftime = np.array([
0.686305, 0.149818, 1.611875, 1.077275, 1.553027, 0.286783, 0.234919, 0.255626, 0.536215, 1.420936,
1.979941, 0.816767, 0.970783, 3.376077, 1.407218, 0.229477, 2.821243, 1.598966, 0.661166, 0.291716,
2.421805, 0.264711, 0.419970, 0.994872, 5.248650, 0.493777, 0.036222, 0.039556, 2.225511, 1.896816,
1.562481, 2.080967, 0.062462, 0.308574, 0.854363, 1.086975, 0.183905, 0.877297, 0.166353, 1.346992,
3.303843, 0.723761, 0.043173, 1.635107, 1.022373, 1.565542, 0.734400, 1.705071, 1.527256, 1.921497,
1.854679, 0.310276, 2.424571, 0.515172, 1.251790, 1.054940, 0.010267, 1.079949, 0.136024, 0.466943,
1.348637, 0.113960, 2.535242, 0.762922, 0.432438, 0.666299, 0.862624, 0.479771, 0.397440, 1.493170,
0.661091, 0.540539, 1.355944, 0.773167, 3.902563, 0.117417, 1.786273, 0.072698, 0.259388, 2.092709,
0.229584, 0.490496, 0.425987, 0.335195, 0.697602, 0.097860, 0.917998, 0.174528, 0.680717, 1.835194,
2.997399, 1.937913, 0.520418, 1.653625, 2.238665, 0.149357, 0.720766, 0.096726, 0.831950, 1.003850,
])
fstatus = np.array([
1, 2, 0, 2, 2, 1, 0, 2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 1, 0,
2, 1, 1, 1, 1, 2, 0, 0, 1, 0, 1, 1, 1, 2, 0, 0, 1, 2, 0, 1,
2, 1, 2, 0, 2, 0, 0, 2, 1, 2, 1, 1, 2, 1, 0, 2, 0, 2, 1, 2,
0, 2, 2, 1, 2, 1, 2, 2, 1, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 0,
0, 0, 1, 1, 2, 1, 2, 2, 1, 0, 1, 1, 1, 2, 1, 2, 2, 0, 1, 1,
])
group_code = np.array([
3, 1, 3, 1, 2, 2, 2, 3, 2, 2, 1, 2, 2, 3, 2, 3, 1, 1, 2, 1,
1, 3, 2, 3, 1, 1, 1, 1, 3, 1, 1, 2, 3, 2, 3, 1, 3, 2, 1, 3,
3, 3, 3, 2, 2, 1, 1, 2, 2, 2, 3, 1, 1, 3, 2, 3, 3, 1, 1, 2,
1, 3, 2, 1, 2, 3, 1, 1, 3, 3, 3, 1, 2, 3, 1, 1, 2, 2, 3, 3,
2, 3, 3, 2, 2, 2, 2, 3, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1,
])
group = np.array(["a", "b", "c"])[group_code - 1]
strata = np.array([
1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1,
2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2,
2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 2, 1, 2, 1,
1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1,
1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2,
])
cov1 = np.array([
np.nan, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0,
1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0,
1.0, 1.0, 1.0, np.nan, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0,
0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0,
1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, np.nan, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0,
1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0,
0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0,
0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0,
1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0,
0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0,
1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0,
0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0,
1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0,
1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0,
0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0,
0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0,
1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0,
1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0,
1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0,
1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0,
1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0,
1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0,
0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0,
0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0,
]).reshape(100, 3)
cov2 = np.column_stack([cov1[:, 0], cov1[:, 0]])
cengroup = cov1[:, 2]
ci = cuminc(ftime, fstatus, group=group, strata=strata)
print("cuminc curve keys:", list(ci.curves.keys()))
print("Gray's k-sample tests (stat, pv, df):\n", ci.tests)
fit = crr(ftime, fstatus, cov1=cov1, cov2=cov2, tf=tf_quad, cengroup=cengroup)
print("crr coefs:", fit.coef)
print("crr converged:", fit.converged)
pred = predict_crr(
fit,
cov1=np.array([[1.0, 1.0, 1.0], [0.0, 0.0, 0.0]]),
cov2=np.array([[1.0, 1.0], [0.0, 0.0]]),
)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
plot_cuminc(ci, ax=ax1)
ax1.set_title("cuminc(ss, cc, gg, strt)")
plot_predict(pred, ax=ax2)
ax2.set_title("predict(crr(ss, cc, cv, cov2, tf, cengroup=cv[,3]))")
plt.tight_layout()
plt.show()
What's different from R's cmprsk
pycmprsk is a port of the functionality, not a verbatim translation of the API:
- Returns dataclasses (
CRRResult,CumincResult,SummaryCRR) rather than R's named lists. Field names use Python snake_case (n_missingvs. R'sn.missing,loglik_nullvs.loglik.null). tf(the time-varying covariate function) takes and returns NumPy arrays; Python's contract is that it returns shape(ndf, p2)(R'scmprskwraps 1D output viaas.matrix; do the equivalent with.reshape(-1, 1)).na.actionis fixed to "omit rows with any NA," matching R's default.
Behavioral parity is the explicit design goal - see src/tests/test_parity.py.
Parity testing
The test suite is 1:1 with R's cmprsk/tests/test.R: every scenario in
that file has a corresponding .npz fixture (data + R reference outputs)
under src/tests/fixtures/, and one matching Python test per scenario.
To regenerate the fixtures (requires R with the cmprsk and reticulate
packages installed):
Rscript src/tests/r_fixtures.R
To run the parity tests:
pytest src/tests
License
pycmprsk is distributed under the GNU General Public License v3.0 or
later (GPL-3.0-or-later).
This package is a derivative work of R's cmprsk (Bob Gray), which is
licensed under GPL (>= 2). The Fortran sources from cmprsk/src/*.f have
been re-implemented in Python while preserving the original
algorithms. As a derivative work, pycmprsk must remain GPL-compatible.
1 maintainer
Metadata
1 maintainer
Release files for pycmprsk 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pycmprsk-1.0.0.tar.gz | 65.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pycmprsk-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 98.0 kB
Release files / pycmprsk-1.0.0.tar.gz
| Download URL | pycmprsk-1.0.0.tar.gz |
|---|---|
| Size | 65.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
42907d120bb38ae50ab60fbe05f7e0546ffc6622a1f39f265be10552ad53f229
|
|
BLAKE2b-256 checksum How to use checksums |
c0f69bb93d9c41019071d666e89e93e9eb3ed73e35fa4b035c2a19caf222ca4a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on May 23, 2026.
Transparency logRelease files / pycmprsk-1.0.0-py3-none-any.whl
| Download URL | pycmprsk-1.0.0-py3-none-any.whl |
|---|---|
| Size | 32.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
c0ef906f8047cb5a8b6bb009bf7982dfc66702058a1c721181cf3b1dc7b68563
|
|
BLAKE2b-256 checksum How to use checksums |
54420657f731bf4126188f287a0210df8555a8d8a134912ec8e90d57d5d837e0
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on May 23, 2026.
Transparency log
